Motor vehicle operating data collection and analysis

ABSTRACT

A method and apparatus for collecting and evaluating powered vehicle operation utilizing on-board diagnostic components and location determining components or systems. The invention creates one or more databases whereby identifiable behavior or evaluative characteristics can be analyzed or categorized. The evaluation can include predicting likely future events. The database can be correlated or evaluated with other databases for a wide variety of uses.

CROSS REFERENCE TO RELATED APPLICATION

This application is a continuation of U.S. application Ser. No.15/460,550 filed on Mar. 16, 2017 and entitled, “Motor Vehicle OperatingData Collection and Analysis,” which claims priority to U.S. applicationSer. No. 14/310,697 filed on Jun. 20, 2014 and entitled “Motor VehicleOperating Data Collection and Analysis,” which claims priority to U.S.application Ser. No. 11/921,192 filed on Nov. 9, 2012 and entitled“Motor Vehicle Operating Data Collection and Analysis,” which claimspriority to PCT/US2005/019279 filed Jun. 1, 2005 and entitled “MotorVehicle Operating Data Collection Analysis.” All of the aforementionedapplications are incorporated by reference in their entirety herein.

FIELD OF USE

The invention pertains to a method and apparatus for evaluating recordeddata of a driver's operation of a motor vehicle. The invention is notlimited to trucks and automobiles but includes all powered equipmentsuch as boats, airplanes and railroads. The invention utilizes timemarked data that can be correlated with information from separatedatabases, particularly data that is also time marked. The recorded datamay facilitate the vehicle owner monitoring the use of the vehicle byothers, e.g., employees, automobile renters or family members, e.g.,teenage drivers. The recorded data may also provide an objectivebehavioral data collection system for third parties, e.g., life andhealth insurance companies, lending institutions, credit ratingcompanies, product and service marketing companies, potential employers,to evaluate an individual's behavioral characteristics in a real lifeand commonly experienced situation, i.e., driving a motor vehicle.

PRIOR ART

Several commercial mechanisms are available on the market that providemeans to monitor vehicle use. One example is the Alltrackusa productthat relies on a global positioning satellite (GPS) system to trackvehicle operation. Such systems employ a calculating methodology todetermine speed and acceleration by using the position differentialimplied by the GPS. Conversely, Davis Technologies markets the CarChipproduct which is a passive OBD data recorder for hobbyists and carenthusiasts who want to record their engine performance. Theshortcomings of the Alltrackusa ‘GPS only’ application is that actualspeed information is not available during intermittent losses of the GPSsignal, which are frequent. This limits the product's usefulness forcreating a complete dataset suitable for developing a useful andobjective driver safety ratings. The shortcoming of the CarChip productis that the unit does not provide GPS capability and the target marketis for car enthusiasts who want to monitor engine diagnostics. Bothexisting technology developments have the inherent shortcoming of localdata storage and reporting. This feature limits the usefulness of thedata and does not allow for the development of an independent ratingsystem.

U.S. Pat. No. 6,064,970, assigned to Progressive Casualty InsuranceCompany, discloses a method and system for determining the cost ofautomobile insurance based upon monitoring, recording and communicatingdata representative of operator and vehicle driving characteristics. Thesystem includes use of a wireless up-link to a central control stationto communicate ‘triggering events’.

U.S. Pat. No. 6,064,970 defines a methodology for private insurancequotes based on endogenous driver variables that are acquired from thecustomer or collected by the insurance company. U.S. Pat. No. 6,064,970does not teach an apparatus and business process that allows customersto voluntarily create datasets that are then objectively interpreted bya third party and converted to objective safety ratings, much as creditpayments or delinquencies are converted to an objective credit rating,or company debt histories converted to a bond rating. This distinctionis vital in order to promote the adoption of driver monitoringtechnology and guarantee that it is utilized in a manner that promotesthe most societal good, rather than simply being the exclusive purviewof one company's insurance premium pricing structure.

Other devices and methods are disclosed in published patentapplications. Included is the application Ser. No. 10/764,076 assignedto Progressive Casualty Insurance Company filed Jan. 23, 2004. Anotherdevice is disclosed in a published application, Ser. No. 10/281,330assigned to Davis Instruments, and filed Oct. 25, 2003.

The existing systems and devices also ignore the profound behavioralcharacteristics exhibited by drivers in operating motor vehicles, e.g.,aggressiveness or patience, caution or recklessness, compliance withlaws, etc. These characteristics are relevant to each individual'sbehavior in other situations including performance of job duties,behavior in stress, and meeting obligations owed to others. Thesebehaviors cannot be ascertained unless the information is uploaded to acentral server to create a comprehensive database for comparison anddevelopment of useful profiles. Existing technology applications do notcentrally store the data and interpret it in context to provide a usefulservice to society.

SUMMARY

The present invention teaches the evaluation and storing of recordeddate and time stamped operating data (“time marked data”) from a motorvehicle component. It also teaches the subsequent upload to amicroprocessor, CPU or central web-server for objective analysis. It mayalso include real time input to the driver or vehicle owner. The datamay also be location marked and the vehicle data may be correlated withseparate time or location specific data points or databases. Therecording of the data to a separate device can be used in such a manneras to insure a complete data set, minimize fraudulent use, and thusinsure the accuracy and usefulness of said data to third parties.Utilization of the data may be subject to terms of agreements among thevehicle operator, the vehicle owner, insurance companies andunderwriters (health, life or auto, etc.), research professionals,credit reporting agencies, marketing and advertising firms, legalrepresentatives, governmental authorities or other institutions.

Since the data may be time marked with an accurate atomic clock signal,the data can be cross-correlated to another information database that isalso time or location specific. This data could include weather events,construction schedules, sporting events, traffic databases, and othertime or location dependent information that puts the driver operatingdata in context and makes it objectively useful. The datamanipulation-analysis includes assessing the driver's driving behaviorby putting the data in context with the applicable local speed laws,signage, traffic signals, weather, and other geographic dependencies(“GIS” data).

The invention can utilize a variety of currently monitored and publiclyaccessible vehicle information from vehicle systems such as an OBD(on-board diagnostic) or CAN (car area network) data-port. This timemarked data may include vehicle speed, throttle position, oxygen sensordata, etc. This information is sequentially recorded at regularintervals from vehicle onboard diagnostic systems, thereby creating atime marked data set of individual data points. The data set of timemarked sequential data points may include the vehicle's location, forexample as determined by a global positioning system (GPS).

Having multiple sources of vehicle data will insure data accuracy. Forexample, speed can either be inferred from the GPS position and timestamped data by calculating the distance between recorded locations anddividing by the time increment, or by accessing speed values directlyfrom the OBD or similar port. Similarly, the vehicle's odometer readingcan be gathered three different ways: first, it can be accessed throughthe OBD extended dataset if the car manufacturer grants permission,secondly, it can be calculated from the GPS location and time stampeddata, third it can be calculated from the speed data logged directlyfrom the OBD port, then multiplied by the time increment to getdistance. Having multiple sources of data insures data integrity bycrosschecking. Time and location stamping the data allows forcrosschecking against other information databases such as weather,traffic, etc.

This collected data may be transferred to a processor (CPU ormicroprocessor) and may be uploaded to a central web-server forevaluation and storage. The invention utilizes data obtained fromindividual vehicle monitoring and instrumentation devices already builtinto motor vehicles since 1996. The invention can also utilizeinformation from supplemental instrumentation such as GPS devicesinstalled on motor vehicles.

The invention teaches transfer of the time marked information from thecollection system within the vehicle to a CPU or similar processor. Thiscomponent may be within the vehicle or separately located. The inventionteaches flexible, multi stage evaluation of the collected data forvariable factors or criteria. The invention permits a weighted profileto be created that can be correlated to both frequency and severity orsignificance of behavior. This weighted profile is useful because thedata integrity has been insured by multiple sources.

The invention also teaches a business subscription service that can beused in conjunction with the recording/analysis apparatus. The methodallows analytic comparison within groups using collected data fromseparate units. This analysis can allow assessment and comparison of avariety of life style/health factors. The analysis, based uponhistorical and accurate data, can be used in conjunction with otherdemographically relevant information.

The invention also teaches wireless or telemetry communication betweenthe in vehicle components, e.g., data storage or processor, and aseparate processor or other electronic data receiving device, therebyeliminating the need to remove a memory component from the vehicle to adata recording or transfer component.

The invention also teaches the monitoring and recording of data fromonboard cameras and proximity sensors, as well as driver physiologicalmonitoring systems. Also included within the invention is predictivemodeling of future behavior as a function of recorded data an individualdriver compared with other drivers within a database.

BRIEF SUMMARY OF DRAWINGS

The accompanying drawings, which are incorporated in and constitute apart of the specification, illustrate preferred embodiments of theinvention. These drawings, together with the general description of theinvention given above and the detailed description of the preferredembodiments given below, serve to explain the principles of theinvention.

FIG. 1 illustrates a matrix of time marked vehicle data that can beevaluated by the invention.

FIG. 2 illustrates an overview or summary of logic steps of oneembodiment of the invention.

FIG. 3 illustrates starting steps of an embodiment of logic flow stepsthat can be incorporated into the evaluation method of the presentinvention.

FIG. 4 illustrates an embodiment of logic steps that may be taken by theuser for properly logging into the system taught by the invention.

FIG. 5 illustrates logic steps utilized in one embodiment of theinvention that are taken in uploading information.

FIG. 6 illustrates the logic steps utilized in one existing embodimentof the invention for reading and commencing revaluation of uploadedfiles.

FIG. 7 illustrates logic steps incorporated into one embodiment of theinvention wherein uploaded recorded information may signal the end ofone driving event and the start of a separate trip.

FIG. 8 illustrates logic steps utilized to achieve continued calculationof vehicle acceleration from time marked speed data for a single trip.

FIG. 9 illustrates the logic steps utilized by an embodiment of theinvention to continuously evaluate recorded GPS time marked trip dataand correlate data to a separate data base containing street and speedlimit information.

FIG. 10 illustrates the sequential relationship of data evaluation forspeed, acceleration, and etc. infractions.

FIG. 11 illustrates the detailed logic steps for determining a speedviolation from each time marked data point of vehicle speed with thematrix of recorded information and the assessment of penalty points forthe Driver Safety Rating.

FIG. 12 illustrates the detailed logic steps for continuous evaluationof compute vehicle acceleration and assessment of penalty points for theDriver Safety Rating.

FIG. 13 illustrates the detailed logic steps for evaluation of a “timeof day violation” in recognition that driving after sunset is inherentlyless safe than driving in daylight.

FIG. 14 illustrates the logic steps for continued evaluation of the timemarked GPS and vehicle speed data in correlation with a separatedatabase containing road sign information to verify, for example, thatthe vehicle has been operated in compliance with a stop sign.

FIG. 15 illustrates the logic steps of an embodiment of the inventionwherein the Driver Safety Rating (DSR) is calculated.

FIG. 16 illustrates the logic steps for deduction of penalty points fromthe DSR.

FIG. 17 illustrates the deduction of past penalty points from acalculated DSR for a separate and later driving event.

FIG. 18 illustrates the application of past penalties utilizing aweighting scheme based upon penalty weight inverse to elapsed time.

FIGS. 19A through 19D comprise a table of actual recorded time markedspeed data and assessed violation/penalty utilizing an embodiment of theinvention.

FIG. 20 illustrates the home page displayed to a user of an embodimentof the invention that incorporates the logic flow sequences illustratedin FIGS. 2 through 18 herein.

FIG. 21 illustrates the log in page displayed to a user of an embodimentof the invention.

FIG. 22 illustrates the screen page displayed to the user after logginginto the invention and allowing the user to select among multipledrivers having recorded driving data uploaded within the database of theinvention.

FIG. 23 illustrates the screen display allowing the user to view variousdriving events of the selected driver that are within the inventiondatabase and for which a Driver Safety Rating has been computed.

FIG. 24 illustrates the screen display providing the type of violationand computed DSR for each violation types for a selected trip.

FIG. 25 illustrates the screen display of evaluated trip data derivedfrom the matrix of time and location marked data.

FIG. 26 illustrates a map of the actual travel of the vehicle asrecorded and evaluated based upon several databases utilizing the timemarked and location marked data.

FIG. 27 is a representation of the display screen of the inventionshowing the streets traveled during a selected driving event (trip) aswell as the time and speed limit.

It will be appreciated that the foregoing drawings illustrate only oneembodiment of the invention and that numerous other variations may becreated within the scope of the described invention.

DETAILED DESCRIPTION OF INVENTION

The above general description and the following detailed description aremerely illustrative of the subject invention and additional modes,advantages and particulars of this invention will be readily suggestedto those skilled in the art without departing from the spirit and scopeof the invention.

The invention comprises multiple steps, beginning with the collection ofdata at regular time intervals, preferably at least as frequently asapproximately every two seconds. The data includes the publiclyavailable operational data from an industry standard port such as aSAE-1962 connector, or an on board diagnostic (“OBD”) port or othervehicle data acquiring component. For example, operation data accessiblevia the OBDI I port includes speed and engine throttle position or othervariable power controls of the vehicle power source. It may also includeso called “extended OBDII” or OBDIII datasets that are specific to eachmanufacturer and also available with manufacturer permission such asodometer reading, seat belt status, activation of brakes, degree andduration of steering direction, etc., and implementation of accidentavoidance devices such as turning signals, headlights, seatbelts,activation of automated braking systems (ABS), etc. Other informationregarding the operation of the vehicle can be collected since theextended OBDII set includes a whole host of engine or other power sourcediagnostic variables.

The invention includes the capability to recognize the particularlanguage emitted by the vehicle system and may configure the recordingcomponent to receive or convert data in SAE J1850, ISO IS09141 or KWP2000 formats. Alternatively, this step may be performed by a processorafter the data is recorded.

Further the invention applies to other data systems being developed andimplemented. An example is the CAN (car area network). Additionally,data from devices or systems that, for example, provide a lane departurewarning, may be recorded. Such systems incorporate one or more camerasintegrated with other sensors to analyze vehicle speed and other factorsto monitor the distance between the vehicle and roadway lane dividerlines. Data also can be recorded from systems that combine laser sensorsand digital rangefinders to scan the road and detect vehicles or otherobjects ahead. Such systems (“active cruise control”) can providewarning or directly reduce speed or activate braking systems. Sensors orrangefinders may similarly detect the presence and distance of objectsbehind the vehicle.

The position and movement of the vehicle can also be collected utilizinga global position system or “GPS” system. Other known locatingtechnologies such as radio frequency tags, cellular telephone networks,or differential GPS may be used. Such technologies are hereinafterreferred to as “GPS” technology or locators.

One embodiment of the invention utilizes data points of various systemsand operations collected at substantially simultaneous intervals,thereby creating sequential “data points” containing information frommultiple sources pertaining to vehicle operation and movement. The datapoints are recorded at regular intervals. These intervals can be ofvaried duration. For purpose of illustration of the invention herein,the intervals are specified to be every two seconds.

The data can be recorded or transferred to various removable electronicstorage devices, including but not limited to flash memory cards nowutilized for digital cameras, etc. Alternatively, recorded data may betransferred remotely via wireless technology currently known asBluetooth®. (The Bluetooth word mark and logos are owned by theBluetooth SIG, Inc.) Other wireless communication systems such ascellular telephone, radio or satellite may be used. These technologiesare hereinafter termed “wireless” transfer or technology.

The data can be transferred to another electronic data reading devicesuch as a microprocessor, a CPU or CPU linked to an Internet server. Therecorded data may also be evaluated by a CPU within the vehicle. Thedata can be transferred, stored, manipulated and analyzed (“evaluated”)as desired to provide information concerning not only the location andduration of vehicle operation, but also the manner in which the vehiclewas operated. For situations where multiple drivers utilize multiplevehicles, each vehicle can be equipped with a non-removable memory torecord all its operation, regardless of which driver utilizes thevehicle. This data can then be reconciled with the data downloaded bythe driver through his or her personal flash memory card. Gaps in thedata can then be investigated by an employer, parent, owner of a rentalvehicle, or otherwise responsible party, i.e., the “user”.

The invention also teaches the recording and evaluation of driverphysiological data, such as heart rate, electrocardiograph (ECG) signalsand blood pressure. For example, ECG signals may be recorded from Polar®sensors located on the steering wheel. (Polar is a registered trademarkof Polar Electro Oy Corporation.)

As suggested in the foregoing summary of invention, that summary beingincorporated by reference within this detailed description of invention,utilization of the data recorded by the invention or the resultingevaluation thereof, may be subject to terms of agreements among thevehicle operator, the vehicle owner, insurance companies andunderwriters (health, life or auto, etc.), research professionals,credit reporting agencies, marketing and advertising firms, legalrepresentatives, governmental authorities or other institutions. Forexample, time and location data may be useful in monitoring thecompliance of a probationer with the terms of probation. It may alsorecorded compliance with a breathalyzer ignition control switch.Equipment rental companies can use the data for ensuring the lessee hascomplied with the terms of the rental or lease agreement. For example,operators that can provide documented compliance may be charged loweruse rates.

FIG. 1 illustrates one embodiment of the type and variety of informationthat may be recorded for evaluation by the invention. The capturedinformation illustrated in FIG. 1 are “Engine on/off” 1, “speed” 2,“throttle” 3, “GPS position” 4, “brake on/off” 5, “headlights” on/off 6,“turn signals” on/off and direction 7, “seatbelt on/off” 8, “c-phoneon/off” 9, and “strng positn” (steering wheel position) 10. Theinvention captures information for each category for each time interval(t₁, t₂, etc.). The collected data is thereby time marked or timestamped. The data may be evaluated for selected and variable criteria.As illustrated in FIG. 2, time marked data of the variety shown in FIG.1, can be acquired 20-1 and uploaded 20-2 into the variable evaluative20-3 algorithm of the invention. The algorithm may be used toobjectively rate 20-4 the data for selected factors of driver safety.Note that not all recorded data is required to be evaluated and thestored data 20-5 can be re-evaluated for differing criteria and factors.Therefore, a database may be created for identifiable and separableindividuals. The database may track driving and other behavior habitsover time.

The operational information may be identifiable to specific operator(s)and include time stamped data and geographic location. Operator identitycan be one of many additional data inputs for each time intervalrecording in FIG. 1. Further, comparison of recorded speeds at differingdata points can provide information regarding vehicle acceleration orde-acceleration (rate of acceleration). As indicated, these calculationscan be inferred from GPS, or measured directly from the OBD port toinsure data integrity. Multiple data sources can be used for comparisonor validation of individual recorded data. For example, see FIG. 9discussed infra. Correlation of vehicle speed with vehicle directionalinformation can also be compared to GPS data of the vehicle travel. Theability to analyze and compare various data sources can provide enhanceddata accuracy and validity. The multiple data sources also providecontinuity of information when individual data sources may beinterrupted, such as temporary interruption of a GPS signal. Thiscontinuous monitoring is vital to create objective driver safety ratingsthat include a complete set of the vehicle's operating data. It alsoprovides an enhanced record of driving events. This record, recorded bythe invention, may be valuable in recreating the events prior to avehicle collision or similar event. It may be a useful in the proof ordisproof of fault or liability.

FIG. 3 illustrates starting steps of an embodiment of logic flow stepsthat can be incorporated into the evaluation method of the presentinvention. These steps are implemented after the vehicle operation datahas been collected. The system first queries whether the user is loggedon or connected to a CPU 31. If not logged on, the user is prompted tolog on 32. If logged on, the system uploads files of collected data fromthe vehicle 33. The system may first process and list the trips recordedin the uploaded collected data 34. The system can display the tripdetails 30-5, including trip map 36.

FIG. 4 illustrates an embodiment of logic steps that may be taken by theuser for properly logging into the system taught by the invention.Properly logging into the system begins at the log in page 32-1. Anexample of a log in page is illustrated in FIG. 21. The user can beprompted to enter the user name and password and then to click on the“Log-in button” 32-2. The system then checks the log in information inthe database to validate the user. After being validated, the user canbe directed to the “Upload File of Collected Data From Vehicle” 33. (SeeFIGS. 3, 21 and 22.)

FIG. 5 illustrates logic steps utilized in one embodiment of theinvention that are taken in uploading information. The user can selectthe driver of interest from the driver names contained in the database.33-1. The file page for the selected driver(s) is then displayed 33-2and the user can be prompted to upload the information pertaining to theselected driver into the system. See for example FIG. 23, illustrating ascreen display that allows the user to view various driving events ofthe selected driver that are within the invention database. Theinformation can then be collected and uploaded 33-4. The system can thensave the information about the trips to the database 33-5. The user canthen be directed to the list trips screen (See FIG. 3)

FIG. 6 illustrates the logic steps utilized in one existing embodimentof the invention for reading and commencing revaluation of uploadedfiles. The logic may first provide reconciliation between the local timezone and the UTC time 34-1. The logic sequence then can query whetherthe system has finished reading the uploaded file 34-2. If the user'ssession is not completed, the reading of a new trip can begin. Thereading commences at a new point on the uploaded file 34-4. The logicsequence queries whether the uploaded file indicates that a new trip hasbegun 34-6. (See FIG. 7.) If a new trip has not begun, the logicsequence continues reading at a new point on the uploaded file andthereby continuing the review of the trip file. If the uploaded dataindicates a new trip has commenced, logic sequence then evaluates thetrip. Evaluation can include for example, calculating the accelerationfor the trip 34-5, obtaining the street names and posted speed limits347, identification of violations (e.g., excess speed andacceleration/deceleration) 34-8 and calculation of a DSR rating 34-9.After completing the trip DSR, the system returns to the uploaded file34-2. If there are no unread files, the information, includingcalculations, is stored in the database 33-5.

FIG. 7 illustrates logic steps incorporated into one embodiment of theinvention wherein uploaded recorded information may signal the end ofone driving event and the start of a separate trip. The sequenceillustrates one embodiment of the logic steps determining whether a newtrip begins. (See FIG. 6, item 34-6.) The system queries 35-1 whetherthere is more than a minimum time gap in the recorded data. If yes, thelogic program classifies the new information to be part of a separatenew trip' 34-3. If there is no gap in recorded data, the system querieswhether there has been a change in vehicle location 35-2. If there is nominimum gap of OBDII data but the GPS location data is unchanged formore than the minimum time 35-2, the new GPS data begins a new trip34-3. (For example, if the car is parked for more than the minimum time,e.g. 15 minutes, with the engine idling, resumed movement of the vehicleafter the 16th minute of engine idling, i.e., the vehicle enginecontinuously operating, would start a new trip.) Until there is morethan a minimum time gap in engine (OBD) data or change in vehicleposition, a new trip is not deemed to start and the logic continues toread the data as new data of a continuing trip 34-4.

FIG. 8 illustrates logic steps utilized to achieve continued calculationof vehicle acceleration from uploaded time marked speed data for asingle trip. As the trip continues 35-4, the next speed data pointcreates a new pair of data points, i.e., the prior data point and thecurrent new speed data point 35-5. The logic program calculates theamount of time 35-6 and the change in speed between the two speed datapoints 35-7. The change is speed per unit of time is the vehicleacceleration 35-8.

FIG. 9 illustrates the logic steps utilized by an embodiment of theinvention to continuously evaluate recorded GPS time marked trip dataand correlate data to a separate database containing street and speedlimit information. The logic program continues from the FIGS. 6 and 7(see item 34-6 in FIG. 6). If the trip is not finished 35-4, the nextdata point is evaluated whether it contains valid GPS data 35-11. Ifyes, the logic system accesses a separate database containing road orstreet information. After determining the nearer road segment 35-12, thestreet name and posted speed limit for that identified road segment isobtained from the database 34-6. The logic system again determineswhether the trip has been finished 35-4 and if yes, correction is madefor crossing street error 35-9. For example if data point t1 isdetermined to be nearest Jones Street with speed limit 45 mph and datapoint t₂ is determined to be the intersection of Jones and Smith Streetswhere Smith Street has a speed limit of 35 mph and at data point t₃ isdetermined to be at Jones Street with the continued speed limit of 45mph, no speed violation will be identified 34-7, assuming, of course,that the driver is operating at 45 mph or below. (Reference is also madeto the collection of data points in FIG. 1.)

FIG. 10 illustrates the sequential separate relationship of dataevaluation for speed, acceleration, etc., infractions. The sequenceillustrates the evaluation of uploaded data for speed violations 36-1,acceleration violations 36-2, time of day violations 36-3 (i.e.,“deductions” to the DSR for driving at night or high risk weekend timesegment), and sign adherence violations 36-4. It will be appreciatedthat the sequence is illustrative only and may be abridged, supplementedor reordered.

FIG. 11 illustrates the detailed logic steps for determining a speedviolation from each time marked data point of vehicle speed with thematrix of recorded information and the assessment of penalty points forthe Driver Safety Rating. The logic program evaluates the uploaded datato determine whether the trip is finished 35-4. If not, the logicprogram obtains the next point having a valid GPS and engine data 35-9.(Reference is made to FIG. 9, items 35-4, 35-10, 35-11.) The logicprogram next queries whether the vehicle speed exceeds the posted limit36-5. If the posted speed limit is not exceeded, there is no currentviolation 36-6. If the speed exceeds the posted limit 36-5, the logicprogram queries 36-8 whether the vehicle is operating at in concurrentviolation, e.g., high-risk driving time violation, accelerationviolation, etc. If the concurrent violation is of the same type 36-9i.e., speed violation, the vehicle will be deemed to be operating in acontinuing speed violation and DSR point deduction increased 36-10. Ifnot of the same type 36-11, a separate DSR deduction will be calculated.The logic program then again queries whether the trip is finished 35-4.It will be appreciated that this logic sequence may be separate from adetermination of whether a selected vehicle operating speed, e.g., 58mph, is ever exceeded.

FIG. 12 illustrates the detailed logic steps for continuous evaluationof vehicle acceleration and assessment of penalty point(s) to the DriverSafety Rating. This logic step, which is separate from the speedviolation step (reference to FIGS. 10 and 11) starts at the same point35-4 and 35-9 (reference again to FIG. 9). The vehicle acceleration isseparately calculated as illustrated, for example, in FIG. 8 discussedabove. Continuing with FIG. 12, the logic program queries 37-1 whetherthe acceleration exceeds a specified limit. If no, there is adetermination 37-2 of no current excess acceleration violation and thelogic program returns to the beginning step 35-4. If the specified“x-limit” rate of acceleration 37-1 is being exceeded, the logic programqueries 37-3 whether there is a concurrent violation. If there is aconcurrent violation, the logic program 37-4 queries whether theviolation is of the same type (e.g., continued acceleration in excess ofthe specified limit) and if yes, the DSR deduction is increased 37-7. Ifthe is no concurrent violation, the logic program continues 37-5 andqueries whether the vehicle speed is in excess of a specified limit. (Itwill be appreciated that a vehicle has a relatively high rate ofacceleration in the first moment of movement from a stopped position,but simultaneously has a relatively slow speed.) If the speed is not inexcess of the specific “x” limits, there is no violation (currentviolation=null) 37-6. If the vehicle speed exceeds the specified limit37-8 (which may differ from the posted speed limit for the road segmentas determined with reference to FIGS. 9 and 11), a new concurrentviolation is assessed. The new current violation type is then determined37-9 depending upon the acceleration. The logic program then repeats andreturns 35-4 to the query of whether the trip is finished.

FIG. 13 illustrates the detailed logic steps for evaluation of a “timeof day violation” in recognition that driving after sunset is inherentlyless safe than driving in daylight. The logic program first ascertainswhether the trip is finished 35-4. If not, the, the logic programobtains the next point and engine data 38-1. The logic program nextqueries if the speed is greater than 0 and local time is greater than“after sunset” 38-2. If no, there is no violation 38-3 and the logicprogram returns to the beginning 35-4. Alternatively, if the speed isgreater than 0 and the local time is after sunset, the logic system nextqueries if there is a current violation 38-4. If there is a concurrentviolation (current violation not equaling null), there is an automaticincrease 38-5 to the concurrent violation deduction from the DriverSafety Rating. If there is no concurrent violation 38-4, a new violationis assessed for the time of day violation 38-6 and the type, i.e.,severity, of violation is in this example illustrated to be determinedby the acceleration 38-7 of the vehicle. As an example, if the vehicleis speeding (current violation not equaling null), there is an automaticsurcharge 38-5 to the driver safety rating. If there is no currentviolation, there is a new violation assessed, but if the vehicle isslowing down or at a constant speed (acceleration equal or less than 0)the driver safety rating penalty may be less than if the vehicle isaccelerating.

FIG. 14 illustrates the logic steps for continued evaluation of the timemarked GPS and vehicle speed data in correlation with a separatedatabase containing road sign information to verify, for example, thatthe vehicle has been operated in compliance with a stop sign. In thisexample, the logic system determines the route of the vehicle takenduring the trip 39-1 and all stop signs located on a separate databasecorrelated with the GPS information are identified. The operation (OBD)data for the vehicle is then correlated with the stop sign locations39-2. If there is a stop sign 39-3, the logic program looks at vehicleoperation within a specified distance before the stop sign 39-4 andparticularly the vehicle speed 39-6. If the lowered speed is 0, thelogic program determines the vehicle stopped in compliance to the stopsign and there is no violation. If the vehicle speed does not slow to 0at any location “nearer than ‘X’ ft from stop sign”, the logic programassesses a violation 39-7 based upon failure to stop in compliance withthe sign. The violation type, i.e. severity, is determined depending onthe lower speed value 39-8. For example the penalty to the driver safetyrating will be less if the logic programs determines a “rolling stop” incontrast to the vehicle never slowing below 30 mph, i.e., “running astop sign”. The logic program then returns to the point 39-2 fordetermining if there is another stop sign.

FIG. 15 illustrates the logic steps of an embodiment of the inventionwherein the Driver Safety Rating (DSR) is calculated for an individualtrip. In the illustrated example, the logic program evaluates theviolations assessed for the specific trip 10-1 and calculates the DSRdeduction 10-2. For example, has the driver previously or frequentlyviolated stop signs and has the driver violated stop signs in thecurrent trip now being evaluated? A deduction, e.g., surcharge 10-3 isapplied to the current trip DSR based upon noted persistence inviolations. The DSR for the current trip is calculated based upon thespecific violations 10-4 assessed during the current trip. A totaldriver safety rating is calculated 10-5 based upon the relative durationof speed violations in the current trip, the relative duration withinthe current trip that the vehicle was operated over a selected speed andafter sunset and the relative duration of the trip that acceleration wasabove a specified rate while the vehicle was moving at a specified speed10-2.

FIG. 16 illustrates the logic steps for deduction of penalty points fromthe DSR. The deduction of penalty points is “for violations on thistrip”. The violations are first collected 10-6. The logic program canreview the trip information and collect each violation 10-7 & 10-8. Adeduction is made for each violation 10-9. The logic program alsodetermines if each violation is the last violation of a series ofconsecutive violations 10-10. If yes, the time duration of theconsecutive violation is calculated 10-11. The persistence for theviolation proportional to the duration of the consecutive violation iscalculated 10-12.

FIG. 17 illustrates the deduction of past penalty points from acalculated DSR for a separate and later driving event. The logic programobtains persistent deductions for the specific driver 10-15. A deductionis applied for each persistent violation 10-16. Past violations aredeemed to be “persistent violations” if there is a sufficient (andvariable) time correlation between the past violation and the violationof the current trip being evaluated. There must be a time overlap or“intersect”.

FIG. 18 illustrates the application of past penalties utilizingweighting scheme based upon penalty weight inverse to elapsed time.Again, however, only violations within or “inside” a specified time zoneare deemed to be persistent violations and factored into the DSR for thecurrent trip. The extent of the “look back” for past violations may varydepending upon the severity of the violations.

In addition to selection of identifiable vehicle operators, theinvention will allow for recording and evaluation of multiple separatetrips by a selected driver. The separate trips can be separated by tripsof longer than a specified duration, trips in which there are multiplebraking events per selected period of time, trips on weekends or atnight, in contrast to morning commutes. Also the trips may be separated,evaluated and contrasted over time. Of course, numerous other variationsmay be implemented and are within the scope of this invention.

The driver safety rating (DSR) score of one embodiment of the inventionmaybe a composite number comprising subscript or superscript notation.For example the subscript may indicate the number of driving eventsevaluated in creating the rating score. It may alternately provide thepercentage that is Interstate, controlled access highway driving. Inanother embodiment, the score may contain a superscript notationindicating the number of recorded severe driving violations, e.g.,operating over 90 mph.

It will be readily appreciated that changes in sequentially recordedvehicle speed can be used to calculate the rate of vehicle acceleration.See FIG. 8. Changes of vehicle position between intervals where there isno recorded vehicle speed, particularly in conjunction with immediateprior de-acceleration, may indicate that the vehicle is skidding.Minimal change in vehicle position relative to rapid acceleration mayindicate the vehicle is being operated without sufficient traction,i.e., “spinning the wheels” or “pealing rubber”.

Operation of the vehicle without headlights, changes in vehicledirection without turn signals, etc. may also be recorded. The frequencyand degree of changed vehicle direction per unit of distance traveledcan indicate lane weaving or, alternatively, driving on a winding road.The vehicle speed, calculated rate of acceleration/de-acceleration,number and duration of brake activation can all be correlated to assessthe operator's performance and driving behavior. Frequent changes invehicle speed and braking events may be indicative of aggressive drivingsuch as tail gating slower moving traffic and lane weaving. Since thedata is collected centrally, comparisons can be made between drivers anddriver profile types can thus be created.

In one embodiment of the invention, the evaluation of data comprisesevents of vehicle speed, compliance with traffic signs and signals,vehicle acceleration and 20 time of day. See FIG. 10.

Current driving behavior may be predictive of future driving behavior.Driving behavior can be assessed from a history of driving infractions,e.g., speeding tickets, and from motor vehicle accident histories. Alsoincluded within the invention is predictive modeling of future behavioras a function of recorded data an individual driver compared with otherdrivers within a database. The predicted likely future behavior may befuture driving or, with careful or sophisticated evaluation of data, maybe predictive of other behavior.

The invention includes creating a database of multiple drivers. Theinvention also includes categorizing driving conditions of similarnature, thereby allowing performance of multiple drivers at differingtimes and locations to be grouped and compared. For example, segments ofa trips occurring on a multi-lane divided and limited access highwayscan be grouped and evaluated. The road type may be determined bycombining GPS data and separate databases showing the number of trafficlanes, exit and entrance points, etc. Alternatively, road type may bedetermined solely by accumulated trip recorded time sensitive GPS andoperational data, such a vehicle direction, speed, braking, andacceleration. Congested urban traffic conditions can be identified bytime and location and categorized. Identification may includeconsideration of the number of drivers within the database proximate toparticular locations at particular times relative to other locations.This may be termed “use” or road use.

Typical or average driving patterns can be identified within suchcategories of road type. Comparison of an individual driver'soperational data to the average or typical operation profile can be madeand deviations noted. With an adequate database, other types of drivingconditions or road types may be identified and categorized. Individualdriver operational data can be compared with the typical or averagedriver profile. Information from such comparisons can be combined andevaluated with demographic variables or other recorded factors andseparate database information such as driver age, sex, marital status,purchasing and credit histories, etc. Evaluation can also be madebetween the driving profile and history of driving infractions oraccidents.

The combined data and evaluations can be useful in predicting likelyfuture behavior, including differing lifestyle and employmentenvironments. In addition, categories of driver personality type can becreated and an individual can be matched with one or more categories.The measurement of relationship strength of an individual to a categorymay utilize standard deviations of predicted co-occurrence orlog-likelihood ratios.

Since the invention included creation of a comprehensive databasewithout prior filtering or evaluation, it is possible for example, torevise or adjust one or more algorithms used in an evaluation. It ispossible to similarly make changes in the evaluative technique ormethodology. This can result, for example, in achieving enhancedpredictive analysis. Predictive results can be compared to actualresults and the technique refined to achieve greater consistency oraccuracy.

An individual driver may also be categorized by the absolute amount oftime the driver is identified to be operating within a road category ortrip segment. Also, an individual driver may be evaluated by therelative portion of each trip that is within a road category. Driving in“off peak” times may differ from “rush hour” vehicle operation.Similarly, predictions of likely future behavior may vary with driversoperating vehicles at differing times or on differing road types.

Changes in an individual driver's profile may be noted and may besuggestive of a change in life style or employment. This may becorrelated to spending and credit histories. Time sensitivity canenhance the predictive value of a profile.

Evaluation of discrete trip segments, in contrast to evaluation ofoperation for an entire trip can also enhance the predictive value. Forexample, all trips that include a first GPS determined point A and thenpoint B within a five minute window and occurring between 8:00 AM and8:30 AM on one or more specified dates may capture all the driversoperating a vehicle in a certain direction of a major arterial roadwayon a “rush hour” morning. Operation on other and differing road segmentsmay not be of value. In this limited “like” environment, it will berelatively easy to identify drivers whose speed, braking andacceleration pattern differ from the average. It will also be relativelyeasy to identify “aggressive: driving. A pattern of aggressive drivingmay be correlated to “risk taking” in other life or employmentenvironments, including but not limited to spending and debt repayment.The evaluation may be further enhanced by tracking the changes invehicle direction within the road segment, i.e., the driver's proclivityto change lanes.

This level of evaluation of individual driver behavior can also bereflected in the driver's safety rating score. It may be useful to havesuch information separately recorded as a subset of a composite score.Driver's that have an “aggressive” driving profile or that frequentlyoperate on “high risk” road segments and/or times can be therefore bereadily identified and distinguished from otherwise similar drivers. Inthe preferred embodiment, the aggressive driver score would be separablefrom the “high risk” road segment driver.

It will be readily appreciated that vehicle driving is a common activityof most individuals over the age of 16. Although driving and trafficconditions vary widely, it may be appreciated that common behaviortraits may be exhibited through vehicle operation. It will be readilyappreciated that an individual that can demonstrate a history of prudentdriving in combination with prudent spending and use of credit may bepart of an ideal target market of certain goods or services. Otherdrivers may choose not to provide such vehicle operation data forvarious reasons. These reasons can include that concern that theinformation would demonstrate less than ideal behavior, such asperceived high risk driving characteristics. For some purposes, it maybe useful to exclude those individuals from the evaluation. Thereby thedatabase is not flawed by their absence. For other purposes, such absentindividuals that are otherwise identifiable may constitute the targetaudience or market. Again, the database is not flawed. For example, aperson having a certain high spending and credit profile, but notreporting vehicle operations data may be particularly receptive to an adcampaign for luxury sports cars or certain vacation travel. The abilityto identify or merely the enhanced ability to identify members of atarget segment will be a valuable tool.

Another aspect of the present invention is to identify events orbehavior that have a strong co-occurrence index or similar frequency ofoccurrence. For example rapid acceleration may frequently occur withhard braking. It may also occur with closely following other vehicles.Frequent lane changes without activating turning signals may becorrelated with rapid acceleration but lane changes with use of turningsignals may not have a similar correlation. However, frequent lanechanges without turning signals on congested urban corridors during rushhour may have a different correlation compared to frequent lane changeswithout turning signal during off peak hours on the same type roadway.The latter may be correlated to with excessive speed while the former isnot.

In another example, a driver operating a vehicle primarily on suburbanstreets during daytime hours may have minimal correlation to excessivespeeding. Conversely, such driver may have minimal demographic oreconomic commonality to drivers that demonstrate excessive speeding. Itmay be useful to exclude both from an evaluation. Therefore being ableto determine where and when the driving occurs may be as important ashow it occurs.

Further, the invention allows behavior or characteristics of drivers tobe compared to other driver, independent of other factors. For example,all vehicles on a congested roadway may be operating below a postedspeed limit. However, some drivers may be exhibiting frequent lanechanges without turn signals, accompanied by high acceleration, hardbraking and tailgating. No driver is operating above the speed limit,but some are exhibiting high-risk behavior. In another example, acomparison of drivers on the same road segment during a recorded rainevent can be compared. How a driver is operating in comparison to theother drivers during the rain event may be more predictive of behaviorthan adherence to posted speed limits.

Another aspect of the invention is the enhancing the predictability oflikely future events by identifying the most predicative characteristicswithin the database and match the occurrence of one or morecharacteristics within the data set of an individual. A scaled score canbe developed for the individual based upon the individual's dataset.

For example, none of a subset of drivers who are identified asprincipally driving on suburban streets may have traffic infractions.However, some drivers within the group may have recorded multiple eventsof “rolling stops” at stop signs. Some drivers may have multiple eventsof changing direction without using turning signals. Others mayfrequently drive without seat belts. Over time, one or more of suchcharacteristics may be strongly correlated to other significant behavioror behavior of interest such as high-risk life style behavior, whetherdriving related or otherwise. Other factors may not show a strongcorrelation with other behavior of interest and may be discounted.Drivers identified as driving with significant frequency on congestedurban arterial roads may be shown to have a correlation with otheraspects of behavior. Therefore, over time some behavior may be shown tohave a strong correlation with other behavior. The other characteristics(having a low index of frequency of correlation) may be thereafterdiscounted as predictive of the correlated behavior of interest.

As suggested above, another aspect of the invention is to identify andutilize characteristics that can be identified by sophisticatedevaluation of the database that focus on prediction of responsiveness tocertain input, e.g. an ad campaign or new product, in contrast to theodds of a future traffic accident or infraction. Such evaluation mayinclude correlation of separate databases.

It will be further appreciated that evaluation of these additional oralternative variables will require minimal adjustment to the logic flowdiagrams (FIGS. 3 through 18). For example, driving after selected timeson Friday and Saturday evenings may be rated independent of othervariables since these times may be statistically the most dangeroustimes. Again, the time of vehicle operation, and designation of thedriver, will be included in the data set of the preferred embodiment.

FIGS. 19A, 19B, 19C and 19D comprise a table of actual recorded timemarked speed data and assessed violation/penalty utilizing an embodimentof the invention. FIGS. 19A through 19D comprise a table of data pointscollected from an actual motor vehicle trip 19-1, utilizing OBD and GPScomponents, and evaluated 19-2 by the subject invention. The tablepresents only collected data points in which a speed violation 19-6 wasrecorded. It will be appreciated that the table could present vehiclespeed information for each sequential data point regardless of an excessspeed event (or other recorded vehicle operation characteristic). In theevent depicted in FIGS. 19A through 19D, the trip started at a timeprior to 1:55:29 PM on Dec. 29, 2003. The vehicle speed was collectedevery 2-seconds and the vehicle position was also recorded at the same 2second intervals. Both recording devices utilized atomic clocks toregulate time intervals and synchronization. A database containing speedlimit information 19-4 applicable to the specific road and locationtraveled was accessed by the processor evaluating the data. The actualvehicle location was derived by the GPS supplied information.

For the driving event (“trip”) subject of FIG. 19, the identity of thedriver is disclosed. The actual speed is recorded and compared to theposted speed limit for each time marked interval.

A driver safety rating (DSR) 19-8 is established upon the evaluation ofthe data. In the driving event subject of FIG. 19, only driving speedhaving been recorded as exceeding the pre-selected criteria, i.e.,posted speed limit has been displayed. (See for example 19-3, 19-5 &19-6.)

For example, in the embodiment of the invention illustrated by FIG. 2, adriver safety rating is established by first evaluating the recordeddata of FIG. 1 in accordance with a formula and subtracting theresulting numerical value (σ) from 100 where 100 represents optimallysafe motor vehicle operation. The formula utilized in this embodimentis:σ=(V ² −L ²)/(L{dot over (x)})whereσ=driver safety rating speed violation deductionV=vehicle speed recorded from OBDL=posted speed limit obtained from a GIS database utilizing the GPSlocation stamp for the data interval.x=adjustment factor to normalize the deduction to a basis DSR of 100.

As stated above, the driver safety rating (DSR)=100−σ

In another embodiment, the product of the calculation can be adjusted bya factor (μ) where μ=an adjustment factor for traffic conditions,weather conditions or time of day. It will be readily appreciated thatoperation of a vehicle at a speed in excess of the posted limit may besubject to a greater penalty or evaluative numerical significance ifoccurring in rain, icy conditions, nighttime, etc. Other factors whichmay justify a further adjustment criteria would include operating avehicle in excess of the posted speed in a school zone, during rush houror on roads that have statistically higher accident rates.

It will be further appreciated that the information contained in thetable comprising FIGS. 19A and 19B illustrates the one data collectionsequence that may utilized and recorded on the transferable electronicmemory media and downloaded to a separate processor.

FIG. 20 illustrates the home page displayed to a user of an embodimentof the invention that incorporates the logic flow sequences illustratedin FIGS. 2 through 18 herein.

FIG. 21 illustrates the log in page displayed 21-1 to a user of anembodiment of the invention.

FIG. 22 illustrates the screen page displayed to the user 22-1 afterlogging into the invention allowing the user to select 22-2 amongmultiple drivers having recorded driving data uploaded within thedatabase of the invention.

FIG. 23 illustrates the screen display allowing the user to view variousdriving events 23-1 of the selected driver 23-2 that are within theinvention database and for which a Driver Safety Rating 23-3 has beencomputed.

FIG. 24 illustrates the screen display providing the type of violation24-1 and computed DSR 24-2 for each violation type for a selected trip24-3.

FIG. 25 illustrates the screen display of evaluated trip data derivedfrom the matrix of time and location marked data. FIG. 25 is apresentation of information of the type of information of FIGS. 19Athrough 19D as it may appear on a user's computer screen.

FIG. 26 illustrates a map of the actual travel of the vehicle asrecorded and evaluated based upon several databases utilizing the timemarked and location marked data. FIG. 26 is a presentation of the GPSdata 26-1A, 26-1B, 26-1C. 26-2 & 26-3, collected as part of the data setforth in FIG. 25, as it may appear on the user's computer screen andillustrating the actual route of vehicle travel. The designated path oftravel may be further color coded 26-4 or otherwise marked to show thespecific location of the event of excess speed or other characteristicincluded in the evaluation determining the driver safety rating.

FIG. 27 is a representation of the display screen of the inventionshowing the streets 27-1 traveled during a selected driving event aswell as the time 27-2A & 27-2B and speed limit 27-3. The screen can bemodified to incorporate other information.

Looking at FIGS. 8 and 9, it will of course be appreciated thatsequential data of speed can be used to calculate the rate ofacceleration. This can be either a positive or negative value with anegative value indicating de-acceleration. For example, in oneembodiment of the invention, the evaluation of data may utilize thefollowing formula:Φ=(A−0.6)/(L{dot over (y)})A=(V ₁ −V ₂)/twhere

-   -   Φ=driver safety rating acceleration deduction    -   V₁=vehicle velocity from the previous time interval recorded        from OBD    -   V₂=vehicle velocity from the current time interval recorded from        OBD.    -   t=time increment between data points    -   L=speed limit    -   y=adjustment factor to normalize the deduction to a basis driver        safety rating of 100.    -   0.6=threshold G-Force above which violations are recorded.        As with speed, the acceleration factor may be subject to a        further adjustment (μ) for traffic, road or weather conditions        as well as for time of day, etc.

In another embodiment, the rating may include the operator's adherenceto traffic control signs and traffic signals (Ø). This embodiment willrequire synchronized GPS and OBD data. An example of application of thiscapability would be failure of the vehicle to stop at a geographiclocation, as determined by the combined and time synchronized GPS andOBD data, known to be controlled by a stop sign. This can be viewed asan enhancement of the tracking speed with posted speed limits.

Yet another embodiment may utilize a separate factor (β) for travel atnight or at determined road locations known to have greater accidents.Travel on Interstate highways traversing relatively sparsely populatedand un-congested areas may understandably present different operatingchallenges and demands than equal mileage driven in congested urbanstreets and expressways with greater traffic density, frequently mergingtraffic and changing traffic speed. Similarly, the drivers' behavior, aswell as driving skill, can be measured by the information metrics of thetype depicted in FIG. 1.

In yet another embodiment, the driver safety rating will be weighted toreflect the number of separate operating events or the cumulativevehicle operation marked data that is incorporated in the rating. Arating that is a product of the evaluation of numerous events can beexpected to have a greater accuracy or greater predictive values forother or future behavior.

The driver safety rating comprising an evaluation of multiple factors,e.g., speed, rate of acceleration, sign adherence and time ofday/location, will be an integration of the recorded and derivedfactors. In one embodiment, the DSR will be a deduction of the evaluatednumerical value from a beginning 100 score. The numerical value willfirst require computation of the DSR for each time-marked interval,e.g., each two-second interval for which OBD, GPS, etc., data iscollected for evaluation.

For example, in a simple calculation involving the four variables listedabove, each variable can be given equal weight (with or withoutincorporating modifying factors such as μ). In that case, the deductionfor each time interval (DSR_(INTERVAL)) can simply be expressed as theaverage of the four values for that interval.DSR_(INTERVAL)=(σ+Φ+Ø+β)/4

The DSR_(TRIP) will then be:DSR_(TRIP)=100−(ΣDSR_(INTERVAL))/t

The invention includes altering or adding additional variables andvarying the evaluation as may be selected, utilizing recorded anduploaded data of vehicle operation as taught by this invention.

The evaluation process can also discard old or “stale” information thatmay be expected to no longer have significant predictive value. Thecriteria for discarding data may be a time function only, or incorporatethe quantity of later data collected. The evaluation process can alsoincorporate a persistence factor for events of selected significance.These may be events of driving at speeds in excess of 20 mph over theposted speed limit. The rating evaluation process may retain the data ornumerical values for a longer duration than data or values pertaining todriving less than 10 mph above a posted speed limit. This process canutilize the “severity” value listed in the table of FIGS. 19A through19D.

Additional variable factors that may be subject of analysis include thenumber of changes in rate of acceleration (including de-acceleration)per linear distance traveled, number of changes in vehicle direction perlinear distance traveled, use of seat belts, turning signals, activationof ABS or SRS systems, lane departure warning systems or intelligentcruise control systems, etc. Driver physiological data such as heartrate and blood pressure may be recorded and included in the analysis.

The invention also teaches real time feed back to the driver. This caninclude warnings of driving above a posted speed limit, warning that thevehicle is approaching a stop sign, or the time remaining before atraffic control light is to change from green to yellow or red, etc. Itmay provide notice of construction or other traffic delays. Thisembodiment utilizes real time access correlation and evaluation ofmultiple databases.

The evaluation can also include quantitative assessments, such as anevaluation based upon changes in vehicle direction, determined fromsteering wheel movement, time, and vehicle speed. This can be correlatedwith GPS data for validation as indicated above. The data can then befurther qualitatively assessed for excessive speed during turningevents, excessive lane changes, “tail gating”, etc. The qualitativeassessment can include assigning numerical values for events. Events canbe qualitative distinguished, i.e., an event of excessive driving speed,an event triggering the ABS or SRS system, could have a differing impactthan an event of failure to activate turning signals.

An additional embodiment could include measurement of driver performancefor a driving event or for operation per hour. The measurement can bestored and supplemented by additional driver specific driving events.Therefore changes in driver behavior over time can be evaluated, therebyproviding a current, accurate assessment of behavior. With progressionof time or collected events, it may be possible or advantageous todelete early events and data.

This specification is to be construed as illustrative only and is forthe purpose of teaching those skilled in the art the manner of carryingout the invention. It is to be understood that the forms of theinvention herein shown and describe are to be taken as the presentlypreferred embodiments. As already stated, various changes may be made inthe shape, size and arrangement of components or adjustments made in thesteps of the method without departing from the scope of this invention.For example, equivalent elements may be substituted for thoseillustrated and described herein and certain features of the inventionmay be utilized independently of the use of other features, all as wouldbe apparent to one skilled in the art after having the benefit of thisdescription of the invention.

Further modifications and alternative embodiments of this invention willbe apparent to those skilled in the art in view of this specification.

What is claimed is:
 1. A driving evaluation computing device comprising: a processing unit comprising a processor; a memory unit; and a wireless receiver, wherein the driving evaluation computing device is configured to: receive a first vehicle operational data, of a first vehicle, collected by a first vehicle data acquiring component over a first period of time, wherein the first vehicle operational data comprises time marked vehicle location data; determine a first vehicle operation factor from the first vehicle operational data, wherein the first vehicle operation factor comprises at least one of vehicle speed, vehicle acceleration, and an amount of time spent driving at night; responsive to a determination of the first vehicle operation factor: calculate a driver safety rating using the first vehicle operation factor; and determine whether the first vehicle operation factor deviates from an operational threshold factor; record, in a driver profile associated with the first vehicle, the driver safety rating, wherein the driver profile is stored in a driver profile database; and record, in the driver profile and responsive to a determination that the first vehicle operation factor deviates from the operational threshold factor, a first deviation event; compare the first vehicle operation factor to a corresponding vehicle operation factor from an average driver profile; determine whether the first vehicle operation factor deviates from the corresponding vehicle operation factor; record, in the driver profile and responsive to a determination that the first vehicle operation factor deviates from the corresponding vehicle operation factor, a second deviation event; and display a notification, on a user device associated with the first vehicle, that at least one of the first deviation event and the second deviation event has occurred.
 2. The device of claim 1, wherein the first vehicle data acquiring component comprises a first GPS.
 3. The device of claim 1, wherein the device is configured to: receive a second vehicle operational data of the first vehicle collected by a second vehicle data acquiring component over the first period of time, wherein the second vehicle data acquiring component comprises a second locator distinct from the first vehicle data acquiring component, and wherein the second vehicle operational data comprises a second time marked vehicle location data; and determine the first vehicle operation factor from the second vehicle operational data.
 4. The device of claim 3, wherein the device is further configured to: compare the first vehicle operational data to the second vehicle operational data; determine whether the first vehicle operation data is accurate using the comparison between the first vehicle operation data and the second vehicle operational data; and responsive to a determination that the first vehicle operational data is accurate, calculate the first vehicle operation factor.
 5. The device of claim 3, wherein the device is further configured to calculate a difference between the first vehicle operational data and the second vehicle operational data, and wherein the first vehicle operational data is accurate when the difference does not exceed an accuracy threshold factor.
 6. The device of claim 3, wherein the first vehicle operational data and the second vehicle operational data each further comprise a weather condition.
 7. The device of claim 1, wherein the user device is a mobile telephone.
 8. The device of claim 1, wherein the average driver profile comprises driving data collected from a plurality of other drivers, and wherein the device is further configured to receive the corresponding vehicle operation factor from the driving data in the average driver profile.
 9. The device of claim 1, wherein the first vehicle operation factor comprises average vehicle speed over a first road segment, and wherein the corresponding vehicle operation factor comprises average vehicle speed over the first road segment from the average driver profile.
 10. The device of claim 1, wherein the device is further configured to identify, from the driver profile database, other driver profiles having a deviation event of the driver profile associated with the first vehicle.
 11. A method for evaluating driving of a vehicle comprising: determining a first vehicle operation factor from a first vehicle operational data, wherein the first vehicle operational data is collected by a first vehicle data acquiring component over a first period of time, wherein the first vehicle operational data comprises time marked vehicle location data, wherein the first vehicle operation factor comprises at least one of vehicle speed, vehicle acceleration, and an amount of time spent driving at night; responsive to a determination of the first vehicle operation factor: calculating a driver safety rating using the first vehicle operation factor, and determining whether the first vehicle operation factor deviates from an operational threshold factor; recording, in a driver profile associated with a first vehicle, the driver safety rating, wherein the driver profile is stored in a driver profile database; recording, in the driver profile and responsive to a determination that the first vehicle operation factor deviates from the operation threshold factor, a first deviation event; comparing the first vehicle operation factor to a corresponding vehicle operation factor from an average driver profile; determining whether the first vehicle operation factor deviates from the corresponding vehicle operation factor; recording, in the driver profile and responsive to a determination that the first vehicle operation factor deviates from the corresponding vehicle operation factor, a second deviation event; and displaying a notification, on a user device associated with the first vehicle, that at least one of the first deviation event and the second deviation event has occurred.
 12. The method of claim 11, wherein the first vehicle data acquiring component comprises a first GPS.
 13. The method of claim 11, further comprising: determining the first vehicle operation factor from a second vehicle operational data, wherein the second vehicle operational data is collected by a second vehicle data acquiring component over the first period of time, wherein the second vehicle data acquiring component comprises a second locator distinct from the first vehicle data acquiring component, and wherein the second vehicle operational data comprises a second time marked vehicle location data.
 14. The method of claim 13, further comprising: comparing the first vehicle operational data to the second vehicle operational data; determining whether the first vehicle operational data is accurate using the comparison between the first vehicle operation data and the second vehicle operational data; and responsive to a determination that the first vehicle operational data is accurate, calculating the first vehicle operation factor.
 15. The method of claim 14, wherein determining whether the first vehicle operational data is accurate comprises calculating a difference between the first vehicle operational data and the second vehicle operational data and, wherein the first vehicle operational data is accurate when the difference does not exceed an accuracy threshold factor.
 16. The method of claim 13, wherein the first vehicle operational data and the second vehicle operational data each further comprise a weather condition.
 17. The method of claim 11, wherein the user device is a mobile telephone.
 18. The method of claim 11, wherein the average driver profile comprises driving data collected from a plurality of other drivers, and wherein the method further comprises receiving the corresponding vehicle operation factor from the driving data in the average driver profile.
 19. The method of claim 11, wherein the first vehicle operation factor comprises average vehicle speed over a first road segment, and wherein the corresponding vehicle operation factor comprises average vehicle speed over the first road segment from the average driver profile.
 20. The method of claim 11, further comprising identifying, from the driver profile database, other driver profiles having a deviation event of the driver profile associated with the first vehicle. 